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Intelligent Transportation System Technologies and Applications, 2nd Edition
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Intelligent Transportation System Technologies and Applications, 2nd Edition

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Transportation and Future Mobility".

Deadline for manuscript submissions: 25 August 2025 | Viewed by 791

Special Issue Editors

Dr. Mahjoub Dridi

E-Mail Website
Guest Editor
Laboratoire Connaissance et Intelligence Artificielle Distribuées (CIAD), University Bourgogne Franche-Comté, UTBM, 90010 Belfort, France
Interests: autonomous intersections; transportation systems; traffic control; urban mobility; combinatorial optimization
Special Issues, Collections and Topics in MDPI journals
Dr. Yazan Mualla

E-Mail Website
Guest Editor
Laboratoire Connaissance et Intelligence Artificielle Distribuées (CIAD), University of Technology of Belfort-Montbéliard (UTBM), 90010 Belfort, France
Interests: explainable artificial intelligence (XAI); human computer interaction (HCI); multiagent systems
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Abdeljalil Abbas-Turki

E-Mail Website
Guest Editor
Laboratoire Connaissances et Intelligence Artificielle Distribuées, Université de Technologie de Belfort-Montbéliard, Belfort, France
Interests: connected autonomous vehicles; cooperative driving; artificial intelligence; control theory; urban mobility
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Traffic congestion is among the largest sources of pollution and noise, not to mention an enormous waste of time and energy. Vehicle traffic rationalization and optimization have become mandatory to at least minimize the impact of pollutant emissions and unsustainable fuel consumption in cities and urban areas. Intelligent transportation systems (ITSs) constitute a fertile research area to manage urban traffic in smart cities and improve transportation efficiency, environmental care, and safety. As science harnesses the technological progress in the ITS domain, paradigm shifts are anticipated.

This Special Issue aims to study the various advanced technologies and applications of intelligent transport systems and highlight their contributions in terms of reducing traffic congestion in cities, improving the safety of vulnerable road users, reducing pollution, increasing the attractiveness of cities and thus supporting the economy of cities. Topics of interest include (but are not limited to) the following:

  • Traffic signal management;
  • Autonomous intersection management;
  • Explainable AI and intelligent transportation;
  • Navigation in smart cities;
  • Cloud services for smart mobility;
  • Control and management of electric and hybrid vehicles;
  • Multi-agent systems;
  • Combinatorial optimization;
  • Meta-heuristics;
  • Reinforcement learning;
  • Deep learning;
  • Petri nets modelling and control;
  • Connected vehicles;
  • Cooperative driving;
  • Computer vision and smart transportation systems.

Dr. Mahjoub Dridi
Dr. Yazan Mualla
Prof. Dr. Abdeljalil Abbas-Turki
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • cooperative driving
  • traffic control
  • urban mobility
  • explainability
  • smart mobility

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Published Papers (3 papers)

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Research

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27 pages, 9692 KiB  
Article
Mitigating Urban Congestion: A Cooperative Reservation Framework for Automated Vehicles
by David Yagüe-Cuevas, Pablo Marín-Plaza, María Paz-Sesmero Lorente, Stephen F. Smith, Araceli Sanchis and José María Armingol Moreno
Appl. Sci. 2025, 15(10), 5347; https://doi.org/10.3390/app15105347 - 10 May 2025
Viewed by 223
Abstract
Today’s urban environments are complex, highly congested traffic scenarios that suffer from multiple unsolved problems such as traffic jams and congestion. These problems pose a significant increase in the risks and probability of traffic accidents in modern cities, which have experienced an enormous [...] Read more.
Today’s urban environments are complex, highly congested traffic scenarios that suffer from multiple unsolved problems such as traffic jams and congestion. These problems pose a significant increase in the risks and probability of traffic accidents in modern cities, which have experienced an enormous growth in the number of vehicles. This work introduces a centralized arbitration framework designed for Cooperative Connected Automated Vehicles (CCAVs) to make real-time decisions and resolve conflicts among various driving strategies or behaviors to facilitate resource reservation based on their collaborative actions. Cooperation and arbitration are two of the most important areas of research that seek to provide tools and mechanisms for the optimization and control of traffic flow at critical locations such as intersections and traffic circles. The approach presented, fully implemented on ROS and capable of constructing a software-defined traffic control environment, is able to supervise in a distributed manner how any CCAV operates with the infrastructure, potentially reducing the number of vehicles waiting and harmonizing the traffic flow. The methodology proposed surpasses traditional driver-in-the-loop cooperation by delivering a higher level of automation for collaborative traffic behavior. This approach demonstrably reduces average waiting time by 13% and increases the total utilization of the traffic emplacement by 70% compared to the classic simulated traffic light model. The solution presented was tested on the Carla simulator, with a complete ROS-based vehicle automation solution that provides promising results for CCAV coordination in complex traffic scenarios through a general framework of behavior-based collaboration. Full article
(This article belongs to the Special Issue Intelligent Transportation System Technologies and Applications, 2nd Edition)
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28 pages, 2345 KiB  
Article
Towards Synthetic Augmentation of Training Datasets Generated by Mobility-on-Demand Service Using Deep Variational Autoencoders
by Martin Gregurić, Filip Vrbanić and Edouard Ivanjko
Appl. Sci. 2025, 15(9), 4708; https://doi.org/10.3390/app15094708 - 24 Apr 2025
Viewed by 199
Abstract
The machine learning-based approaches for analysing the mobility needs of users are currently the most prevalent approach in the mobility-on-demand (MoD) analysis. Their efficiency relies on the comprehensiveness and consistency of training datasets. However, this is also the biggest challenge, as high-quality training [...] Read more.
The machine learning-based approaches for analysing the mobility needs of users are currently the most prevalent approach in the mobility-on-demand (MoD) analysis. Their efficiency relies on the comprehensiveness and consistency of training datasets. However, this is also the biggest challenge, as high-quality training data are often difficult to obtain. Thus, the Variational Autoencoders (VAE) are investigated as potential generators of synthetic samples for the augmentation of MoD-based datasets. This MoD-based dataset is created using real-world taxi trip data recorded in the Manhattan district of New York City, USA. This augmentation by synthetic samples can potentially enable larger, balanced, and more consistent datasets for machine learning analysis of MoD-based data. The proposed VAE approaches are compared with common dimensionality reduction techniques and standard autoencoders concerning their efficiency in 2-dimensional clustering based on collected MoD-based data. The proposed 2-dimensional convolution VAE framework has achieved clustering results comparable with the other analysed approaches. Thus, it generates synthetic samples, known as “deepfakes”. They are added in different percentages to the initial dataset based on real-world MoD-based data. Thus, this creates augmented datasets of the initial one. The models for predicting the cluster of each sample are used to evaluate the impact of those augmented datasets on their accuracy and learning convergence compared to the initial dataset. Results have shown that the accuracy and learning convergence are improved if those predictive models are trained on an augmented dataset which includes up to 10% of synthetic samples for each cluster. Full article
(This article belongs to the Special Issue Intelligent Transportation System Technologies and Applications, 2nd Edition)
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Review

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70 pages, 1506 KiB  
Review
Emerging Research Issues and Directions on MaaS, Sustainability and Shared Mobility in Smart Cities with Multi-Modal Transport Systems
by Fu-Shiung Hsieh
Appl. Sci. 2025, 15(10), 5709; https://doi.org/10.3390/app15105709 - 20 May 2025
Abstract
In recent years, several emerging transport modes have appeared in cities all over the world and have been widely adopted by commuters and travelers. This leads to strong growth and popularity of multi-modal transport and Mobility as a Service (MaaS) in cities. These [...] Read more.
In recent years, several emerging transport modes have appeared in cities all over the world and have been widely adopted by commuters and travelers. This leads to strong growth and popularity of multi-modal transport and Mobility as a Service (MaaS) in cities. These emerging transport modes have not only received much attention from service providers and practitioners but have also attracted researchers in related communities. These are reflected in the growing number of published papers related to research issues of multi-modal mobility transport in cities. The factors that have been driving the strong growth of the number of published papers related to the emerging multi-modal transport in cities are the deficiencies of effective solution methods to accommodate the needs of users in cities with multi-modal transport modes. Although the existing literature is still deficient in offering seamless end-to-end multi-modal mobility transport services, it provides valuable sources and clues for finding the potential future research subjects/issues/directions. In this study, we attempt to identify potential research directions based on a review of the existing literature on multi-modal mobility transport. By searching the WOS database, we analyze the profile and trends of research directions related to multi-modal mobility. The results of this study pave the way for the assessment of research subjects/issues/directions under the umbrella term of multi-modal mobility transport. This review paper significantly reduces the time required for readers to identify prospective research subjects, issues, or directions without delving into the literature. Full article
(This article belongs to the Special Issue Intelligent Transportation System Technologies and Applications, 2nd Edition)
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